Applied AI ML Lead

Salary: $61,000 - 101,000 per year Requirements: Masters degree in a data science-related field with 8 years of industry experience, or a PhD in a data science-related field Hands-on experience developing and deploying machine learning and generative AI solutions with Python Proven ability to write and maintain production-grade code with attention to reliability, performance, and maintainability Experience with continuous integration practices and unit test development to support quality delivery Experience building and managing data pipelines and processing workflows for analytics and machine learning use cases Strong written and verbal communication skills, including the ability to connect technical decisions to business outcomes Demonstrated scientific mindset and structured problem-solving approach for ambiguous, data-driven challenges Ability to work independently while partnering effectively across product, engineering, and business teams Experience building large language model applications that use context engineering to improve response quality and reliability Background in semantic modeling, ontology design, and governance practices in enterprise environments Experience designing semantic integration patterns across data contracts and APIs in distributed systems Experience implementing monitoring and evaluation approaches for machine learning and generative AI in production Experience mentoring data scientists and engineers and promoting modern machine learning engineering best practices Responsibilities: Develop generative AI, agent-based AI, and large language model solutions in Python from proof of concept through production deployment Design context engineering approaches to improve model accuracy, latency, reliability, and overall workflow performance Lead semantic modeling strategy, including ontology standards, governance, and lifecycle management aligned to enterprise needs Create scalable ontologies that model business entities, relationships, rules, and constraints in partnership with domain experts Define semantic integration patterns across data pipelines, APIs, data contracts, and experience layers to resolve semantic conflicts Build and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration methods Build and maintain pipelines and frameworks for model training, evaluation, optimization, monitoring, and production operations Implement responsible AI practices, model risk controls, and governance aligned to regulated environments and internal standards Communicate complex technical concepts clearly to technical and non-technical stakeholders, including senior leaders, to align delivery to business objectives Technologies: AI AI Agents Business Intelligence Support Machine Learning Model Training Python Network More: We are working with J.P. Morgan on this Applied AI and Machine Learning Lead role within JPMorganChase Corporate Technology Data Science and AI. You will help shape how analytics and generative AI are delivered at enterprise scale, turning complex business problems into production-ready, measurable solutions. You will join a collaborative team that values engineering excellence, responsible innovation, and continuous learning. We offer a competitive total rewards package, including base salary based on the role, experience, skill set, and location, with eligible roles potentially receiving commission-based pay and/or discretionary incentive compensation. We also provide a range of benefits and programs, such as comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching, and more. Our workplace values diversity and inclusion, and our Corporate Technology team supports applications and tech operations across our corporate functions, including Global Finance, Corporate Treasury, Risk Management, Human Resources, Compliance, Legal, and the Corporate Administrative Office. last updated 36 week of 2026